Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-09-29

AUTHORS

Ji-Young Kim, Dongkyu Oh, Kiyoung Sung, Hongyoon Choi, Jin Chul Paeng, Gi Jeong Cheon, Keon Wook Kang, Dong Young Lee, Dong Soo Lee

ABSTRACT

PurposeAmyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning–based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading.MethodsA total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide).ResultsInter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system).ConclusionOur results highlight the impact of deep learning–based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading. More... »

PAGES

1116-1123

References to SciGraph publications

  • 2001-03. Imaging of amyloid-β deposits in brains of living mice permits direct observation of clearance of plaques with immunotherapy in NATURE MEDICINE
  • 2016-02-09. Cerebral white matter lesions – associations with Aβ isoforms and amyloid PET in SCIENTIFIC REPORTS
  • 2015-05-08. Cognitive and functional patterns of nondemented subjects with equivocal visual amyloid PET findings in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2019-11-20. Evaluation of PiB visual interpretation with CSF Aβ and longitudinal SUVR in J-ADNI study in ANNALS OF NUCLEAR MEDICINE
  • 2019-10-14. Amyloid PET Quantification Via End-to-End Training of a Deep Learning in NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-02-12. Beta-amyloid imaging with florbetaben in CLINICAL AND TRANSLATIONAL IMAGING
  • 2019-12-28. Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2019-12-13. A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-10-08. Cerebral amyloid PET imaging in Alzheimer’s disease in ACTA NEUROPATHOLOGICA
  • 2017-01-07. Quantitation of PET signal as an adjunct to visual interpretation of florbetapir imaging in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2010-03-10. Traumatic brain injury and amyloid-β pathology: a link to Alzheimer's disease? in NATURE REVIEWS NEUROSCIENCE
  • 2016-12-13. Inter-rater variability of visual interpretation and comparison with quantitative evaluation of 11C-PiB PET amyloid images of the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) multicenter study in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00259-020-05044-x

    DOI

    http://dx.doi.org/10.1007/s00259-020-05044-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1131271256

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/32990807


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Physical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0299", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Other Physical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Clinical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Alzheimer Disease", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Amyloid", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Aniline Compounds", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Deep Learning", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Positron-Emission Tomography", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Stilbenes", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412480.b", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
                "Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kim", 
            "givenName": "Ji-Young", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Oh", 
            "givenName": "Dongkyu", 
            "id": "sg:person.0734106454.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734106454.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sung", 
            "givenName": "Kiyoung", 
            "id": "sg:person.016177210415.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016177210415.20"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Choi", 
            "givenName": "Hongyoon", 
            "id": "sg:person.0631257534.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Paeng", 
            "givenName": "Jin Chul", 
            "id": "sg:person.01343511335.32", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01343511335.32"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Radiation Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
                "Institute on Aging, Seoul National University, Seoul, Republic of Korea", 
                "Radiation Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cheon", 
            "givenName": "Gi Jeong", 
            "id": "sg:person.01333472502.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333472502.31"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kang", 
            "givenName": "Keon Wook", 
            "id": "sg:person.0761746266.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761746266.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea", 
                "Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Dong Young", 
            "id": "sg:person.01312707273.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312707273.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Dong Soo", 
            "id": "sg:person.015617314175.88", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00401-013-1185-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003393960", 
              "https://doi.org/10.1007/s00401-013-1185-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12149-019-01420-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122753351", 
              "https://doi.org/10.1007/s12149-019-01420-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-019-04596-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1123348270", 
              "https://doi.org/10.1007/s00259-019-04596-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13139-019-00610-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1121784762", 
              "https://doi.org/10.1007/s13139-019-00610-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/85525", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011711926", 
              "https://doi.org/10.1038/85525"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep20709", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006228607", 
              "https://doi.org/10.1038/srep20709"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrn2808", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028601578", 
              "https://doi.org/10.1038/nrn2808"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-019-04663-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1123708937", 
              "https://doi.org/10.1007/s00259-019-04663-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-015-3067-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000945863", 
              "https://doi.org/10.1007/s00259-015-3067-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40336-015-0102-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002551507", 
              "https://doi.org/10.1007/s40336-015-0102-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-016-3591-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007443607", 
              "https://doi.org/10.1007/s00259-016-3591-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-016-3601-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049877534", 
              "https://doi.org/10.1007/s00259-016-3601-4"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2020-09-29", 
        "datePublishedReg": "2020-09-29", 
        "description": "PurposeAmyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning\u2013based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading.MethodsA total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide).ResultsInter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27\u2009\u00b1\u20090.078 for visual reading-only session vs. 1.66\u2009\u00b1\u20090.63 for a visual reading session with the deep learning system).ConclusionOur results highlight the impact of deep learning\u2013based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00259-020-05044-x", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1297401", 
            "issn": [
              "1619-7070", 
              "1619-7089"
            ], 
            "name": "European Journal of Nuclear Medicine and Molecular Imaging", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "48"
          }
        ], 
        "keywords": [
          "amyloid burden", 
          "inter-reader agreement", 
          "cortical amyloid burden", 
          "visual reading", 
          "deep learning system", 
          "Fleiss' kappa coefficient", 
          "PET readings", 
          "amyloid PET images", 
          "PET images", 
          "noninvasive assessment", 
          "ConclusionOur results", 
          "clinical setting", 
          "first reading session", 
          "ResultsInter-reader agreement", 
          "clinical routine", 
          "learning system", 
          "deep learning-based end", 
          "learning-based end", 
          "scores", 
          "burden", 
          "confidence scores", 
          "reading sessions", 
          "sessions", 
          "kappa coefficient", 
          "support system", 
          "qualitative assessment", 
          "end estimation", 
          "estimation system", 
          "PET", 
          "assessment", 
          "total", 
          "images", 
          "visual interpretation", 
          "setting", 
          "interval", 
          "system", 
          "study", 
          "confidence", 
          "results", 
          "estimation", 
          "information", 
          "experts", 
          "routines", 
          "quantification", 
          "end", 
          "impact", 
          "quantification results", 
          "scale", 
          "readers", 
          "reading", 
          "output", 
          "interpretation", 
          "agreement", 
          "coefficient"
        ], 
        "name": "Visual interpretation of [18F]Florbetaben PET supported by deep learning\u2013based estimation of amyloid burden", 
        "pagination": "1116-1123", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1131271256"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00259-020-05044-x"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "32990807"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00259-020-05044-x", 
          "https://app.dimensions.ai/details/publication/pub.1131271256"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:47", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_856.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00259-020-05044-x"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00259-020-05044-x'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00259-020-05044-x'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00259-020-05044-x'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00259-020-05044-x'


     

    This table displays all metadata directly associated to this object as RDF triples.

    270 TRIPLES      21 PREDICATES      100 URIs      78 LITERALS      14 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00259-020-05044-x schema:about N06d4d28e66374174b8000cc46dbfce83
    2 N19f6cd0753a842d997ec73de1523f30d
    3 N2631a35bd8074f1898d782d475381ec0
    4 N498a5850725a4d71a09a2231e0c0e942
    5 N547df89129824110ab632f9182a7f1b8
    6 N9cd32019d27f41798214e14710d439d1
    7 Na51fb45778a644b0b446387638cb5f1e
    8 anzsrc-for:02
    9 anzsrc-for:0299
    10 anzsrc-for:11
    11 anzsrc-for:1103
    12 schema:author N24cf9b8d49d540b8a08cf661fd0ac470
    13 schema:citation sg:pub.10.1007/s00259-015-3067-9
    14 sg:pub.10.1007/s00259-016-3591-2
    15 sg:pub.10.1007/s00259-016-3601-4
    16 sg:pub.10.1007/s00259-019-04596-x
    17 sg:pub.10.1007/s00259-019-04663-3
    18 sg:pub.10.1007/s00401-013-1185-7
    19 sg:pub.10.1007/s12149-019-01420-2
    20 sg:pub.10.1007/s13139-019-00610-0
    21 sg:pub.10.1007/s40336-015-0102-6
    22 sg:pub.10.1038/85525
    23 sg:pub.10.1038/nrn2808
    24 sg:pub.10.1038/srep20709
    25 schema:datePublished 2020-09-29
    26 schema:datePublishedReg 2020-09-29
    27 schema:description PurposeAmyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning–based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading.MethodsA total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide).ResultsInter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system).ConclusionOur results highlight the impact of deep learning–based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.
    28 schema:genre article
    29 schema:isAccessibleForFree false
    30 schema:isPartOf N0f1322b25af74fb18c9b92323ab5d244
    31 Nc8d099e79d4141fb814d29a5939523a4
    32 sg:journal.1297401
    33 schema:keywords ConclusionOur results
    34 Fleiss' kappa coefficient
    35 PET
    36 PET images
    37 PET readings
    38 ResultsInter-reader agreement
    39 agreement
    40 amyloid PET images
    41 amyloid burden
    42 assessment
    43 burden
    44 clinical routine
    45 clinical setting
    46 coefficient
    47 confidence
    48 confidence scores
    49 cortical amyloid burden
    50 deep learning system
    51 deep learning-based end
    52 end
    53 end estimation
    54 estimation
    55 estimation system
    56 experts
    57 first reading session
    58 images
    59 impact
    60 information
    61 inter-reader agreement
    62 interpretation
    63 interval
    64 kappa coefficient
    65 learning system
    66 learning-based end
    67 noninvasive assessment
    68 output
    69 qualitative assessment
    70 quantification
    71 quantification results
    72 readers
    73 reading
    74 reading sessions
    75 results
    76 routines
    77 scale
    78 scores
    79 sessions
    80 setting
    81 study
    82 support system
    83 system
    84 total
    85 visual interpretation
    86 visual reading
    87 schema:name Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden
    88 schema:pagination 1116-1123
    89 schema:productId N2f250a86d4644e30914c9a4f3fea98a1
    90 N5e2b49d7e49643b19208d1b6945eb2ac
    91 Naa5359be15db499d994982d5748df733
    92 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131271256
    93 https://doi.org/10.1007/s00259-020-05044-x
    94 schema:sdDatePublished 2022-10-01T06:47
    95 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    96 schema:sdPublisher Nfd9163506053458995b5010aca262932
    97 schema:url https://doi.org/10.1007/s00259-020-05044-x
    98 sgo:license sg:explorer/license/
    99 sgo:sdDataset articles
    100 rdf:type schema:ScholarlyArticle
    101 N06d4d28e66374174b8000cc46dbfce83 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    102 schema:name Alzheimer Disease
    103 rdf:type schema:DefinedTerm
    104 N082ca1f82f4047d88ed918461525866d rdf:first sg:person.0631257534.28
    105 rdf:rest Nf4de42b8bd6b4155b47495331d826cae
    106 N09d95fae27a2480b821f4fafb2ddfb7a rdf:first sg:person.0734106454.51
    107 rdf:rest Ne6662d48e1104b06b48348591d6dfd4d
    108 N0b0c82dacef4421c920a1f43ec00a187 rdf:first sg:person.01333472502.31
    109 rdf:rest N8a2a21de4ca24d56bc1c9f0ffea9b26b
    110 N0f1322b25af74fb18c9b92323ab5d244 schema:issueNumber 4
    111 rdf:type schema:PublicationIssue
    112 N19f6cd0753a842d997ec73de1523f30d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    113 schema:name Deep Learning
    114 rdf:type schema:DefinedTerm
    115 N24cf9b8d49d540b8a08cf661fd0ac470 rdf:first N41581f95d559433bba31920a13e42d1d
    116 rdf:rest N09d95fae27a2480b821f4fafb2ddfb7a
    117 N2631a35bd8074f1898d782d475381ec0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    118 schema:name Humans
    119 rdf:type schema:DefinedTerm
    120 N2f250a86d4644e30914c9a4f3fea98a1 schema:name dimensions_id
    121 schema:value pub.1131271256
    122 rdf:type schema:PropertyValue
    123 N41581f95d559433bba31920a13e42d1d schema:affiliation grid-institutes:grid.412480.b
    124 schema:familyName Kim
    125 schema:givenName Ji-Young
    126 rdf:type schema:Person
    127 N498a5850725a4d71a09a2231e0c0e942 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    128 schema:name Stilbenes
    129 rdf:type schema:DefinedTerm
    130 N547df89129824110ab632f9182a7f1b8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    131 schema:name Amyloid
    132 rdf:type schema:DefinedTerm
    133 N5e2b49d7e49643b19208d1b6945eb2ac schema:name doi
    134 schema:value 10.1007/s00259-020-05044-x
    135 rdf:type schema:PropertyValue
    136 N8a2a21de4ca24d56bc1c9f0ffea9b26b rdf:first sg:person.0761746266.86
    137 rdf:rest Ncef0a809a33e4a7b8a312d79c2fff30d
    138 N9cd32019d27f41798214e14710d439d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    139 schema:name Positron-Emission Tomography
    140 rdf:type schema:DefinedTerm
    141 Na51fb45778a644b0b446387638cb5f1e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    142 schema:name Aniline Compounds
    143 rdf:type schema:DefinedTerm
    144 Naa5359be15db499d994982d5748df733 schema:name pubmed_id
    145 schema:value 32990807
    146 rdf:type schema:PropertyValue
    147 Nc8d099e79d4141fb814d29a5939523a4 schema:volumeNumber 48
    148 rdf:type schema:PublicationVolume
    149 Ncef0a809a33e4a7b8a312d79c2fff30d rdf:first sg:person.01312707273.30
    150 rdf:rest Nf3625bb0693b4e7dabe7a07a7b534dda
    151 Ne6662d48e1104b06b48348591d6dfd4d rdf:first sg:person.016177210415.20
    152 rdf:rest N082ca1f82f4047d88ed918461525866d
    153 Nf3625bb0693b4e7dabe7a07a7b534dda rdf:first sg:person.015617314175.88
    154 rdf:rest rdf:nil
    155 Nf4de42b8bd6b4155b47495331d826cae rdf:first sg:person.01343511335.32
    156 rdf:rest N0b0c82dacef4421c920a1f43ec00a187
    157 Nfd9163506053458995b5010aca262932 schema:name Springer Nature - SN SciGraph project
    158 rdf:type schema:Organization
    159 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
    160 schema:name Physical Sciences
    161 rdf:type schema:DefinedTerm
    162 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
    163 schema:name Other Physical Sciences
    164 rdf:type schema:DefinedTerm
    165 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    166 schema:name Medical and Health Sciences
    167 rdf:type schema:DefinedTerm
    168 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
    169 schema:name Clinical Sciences
    170 rdf:type schema:DefinedTerm
    171 sg:journal.1297401 schema:issn 1619-7070
    172 1619-7089
    173 schema:name European Journal of Nuclear Medicine and Molecular Imaging
    174 schema:publisher Springer Nature
    175 rdf:type schema:Periodical
    176 sg:person.01312707273.30 schema:affiliation grid-institutes:grid.31501.36
    177 schema:familyName Lee
    178 schema:givenName Dong Young
    179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312707273.30
    180 rdf:type schema:Person
    181 sg:person.01333472502.31 schema:affiliation grid-institutes:grid.31501.36
    182 schema:familyName Cheon
    183 schema:givenName Gi Jeong
    184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333472502.31
    185 rdf:type schema:Person
    186 sg:person.01343511335.32 schema:affiliation grid-institutes:grid.412484.f
    187 schema:familyName Paeng
    188 schema:givenName Jin Chul
    189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01343511335.32
    190 rdf:type schema:Person
    191 sg:person.015617314175.88 schema:affiliation grid-institutes:grid.31501.36
    192 schema:familyName Lee
    193 schema:givenName Dong Soo
    194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88
    195 rdf:type schema:Person
    196 sg:person.016177210415.20 schema:affiliation grid-institutes:grid.412484.f
    197 schema:familyName Sung
    198 schema:givenName Kiyoung
    199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016177210415.20
    200 rdf:type schema:Person
    201 sg:person.0631257534.28 schema:affiliation grid-institutes:grid.412484.f
    202 schema:familyName Choi
    203 schema:givenName Hongyoon
    204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28
    205 rdf:type schema:Person
    206 sg:person.0734106454.51 schema:affiliation grid-institutes:grid.412484.f
    207 schema:familyName Oh
    208 schema:givenName Dongkyu
    209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734106454.51
    210 rdf:type schema:Person
    211 sg:person.0761746266.86 schema:affiliation grid-institutes:grid.412484.f
    212 schema:familyName Kang
    213 schema:givenName Keon Wook
    214 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761746266.86
    215 rdf:type schema:Person
    216 sg:pub.10.1007/s00259-015-3067-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000945863
    217 https://doi.org/10.1007/s00259-015-3067-9
    218 rdf:type schema:CreativeWork
    219 sg:pub.10.1007/s00259-016-3591-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007443607
    220 https://doi.org/10.1007/s00259-016-3591-2
    221 rdf:type schema:CreativeWork
    222 sg:pub.10.1007/s00259-016-3601-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049877534
    223 https://doi.org/10.1007/s00259-016-3601-4
    224 rdf:type schema:CreativeWork
    225 sg:pub.10.1007/s00259-019-04596-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1123348270
    226 https://doi.org/10.1007/s00259-019-04596-x
    227 rdf:type schema:CreativeWork
    228 sg:pub.10.1007/s00259-019-04663-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1123708937
    229 https://doi.org/10.1007/s00259-019-04663-3
    230 rdf:type schema:CreativeWork
    231 sg:pub.10.1007/s00401-013-1185-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003393960
    232 https://doi.org/10.1007/s00401-013-1185-7
    233 rdf:type schema:CreativeWork
    234 sg:pub.10.1007/s12149-019-01420-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122753351
    235 https://doi.org/10.1007/s12149-019-01420-2
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1007/s13139-019-00610-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1121784762
    238 https://doi.org/10.1007/s13139-019-00610-0
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1007/s40336-015-0102-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002551507
    241 https://doi.org/10.1007/s40336-015-0102-6
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1038/85525 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011711926
    244 https://doi.org/10.1038/85525
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1038/nrn2808 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028601578
    247 https://doi.org/10.1038/nrn2808
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1038/srep20709 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006228607
    250 https://doi.org/10.1038/srep20709
    251 rdf:type schema:CreativeWork
    252 grid-institutes:grid.31501.36 schema:alternateName Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
    253 Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
    254 Radiation Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
    255 schema:name Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
    256 Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
    257 Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    258 Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
    259 Institute on Aging, Seoul National University, Seoul, Republic of Korea
    260 Radiation Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
    261 rdf:type schema:Organization
    262 grid-institutes:grid.412480.b schema:alternateName Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    263 schema:name Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    264 Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    265 rdf:type schema:Organization
    266 grid-institutes:grid.412484.f schema:alternateName Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
    267 Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    268 schema:name Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
    269 Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    270 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...